scholarly journals An Improved Index for Urban Population Distribution Mapping Based on Nighttime Lights (DMSP-OLS) Data: An Experiment in Riyadh Province, Saudi Arabia

2021 ◽  
Vol 13 (6) ◽  
pp. 1171
Author(s):  
Mohammed Alahmadi ◽  
Shawky Mansour ◽  
David Martin ◽  
Peter Atkinson
Keyword(s):  
Spatial Information ◽  
Population Distribution ◽  
Population Data ◽  
Mean Relative Error ◽  
Nighttime Lights ◽  
Bare Land ◽  
Distribution Mapping ◽  
Nighttime Light ◽  
Saturation Effects ◽  
Population Mapping

Knowledge of the spatial pattern of the population is important. Census population data provide insufficient spatial information because they are released only for large geographic areas. Nighttime light (NTL) data have been utilized widely as an effective proxy for population mapping. However, the well-reported challenges of pixel overglow and saturation influence the applicability of the Defense Meteorological Program Operational Line-Scan System (DMSP-OLS) for accurate population mapping. This paper integrates three remotely sensed information sources, DMSP-OLS, vegetation, and bare land areas, to develop a novel index called the Vegetation-Bare Adjusted NTL Index (VBANTLI) to overcome the uncertainties in the DMSP-OLS data. The VBANTLI was applied to Riyadh province to downscale governorate-level census population for 2004 and 2010 to a gridded surface of 1 km resolution. The experimental results confirmed that the VBANTLI significantly reduced the overglow and saturation effects compared to widely applied indices such as the Human Settlement Index (HSI), Vegetation Adjusted Normalized Urban Index (VANUI), and radiance-calibrated NTL (RCNTL). The correlation coefficient between the census population and the RCNTL (R = 0.99) and VBANTLI (R = 0.98) was larger than for the HSI (R = 0.14) and VANUI (R = 0.81) products. In addition, Model 5 (VBANTLI) was the most accurate model with R2 and mean relative error (MRE) values of 0.95% and 37%, respectively.

scholarly journals Modeling Spatiotemporal Population Changes by Integrating DMSP-OLS and NPP-VIIRS Nighttime Light Data in Chongqing, China

2021 ◽  
Vol 13 (2) ◽  
pp. 284
Author(s):  
Dan Lu ◽  
Yahui Wang ◽  
Qingyuan Yang ◽  
Kangchuan Su ◽  
Haozhe Zhang ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Relative Error ◽  
Urban Areas ◽  
Large Scale ◽  
Population Distribution ◽  
Spatial Optimization ◽  
Distribution Data ◽  
Mountainous Areas ◽  
Mean Relative Error ◽  
Nighttime Light

The sustained growth of non-farm wages has led to large-scale migration of rural population to cities in China, especially in mountainous areas. It is of great significance to study the spatial and temporal pattern of population migration mentioned above for guiding population spatial optimization and the effective supply of public services in the mountainous areas. Here, we determined the spatiotemporal evolution of population in the Chongqing municipality of China from 2000–2018 by employing multi-period spatial distribution data, including nighttime light (NTL) data from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) and the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS). There was a power function relationship between the two datasets at the pixel scale, with a mean relative error of NTL integration of 8.19%, 4.78% less than achieved by a previous study at the provincial scale. The spatial simulations of population distribution achieved a mean relative error of 26.98%, improved the simulation accuracy for mountainous population by nearly 20% and confirmed the feasibility of this method in Chongqing. During the study period, the spatial distribution of Chongqing’s population has increased in the west and decreased in the east, while also increased in low-altitude areas and decreased in medium-high altitude areas. Population agglomeration was common in all of districts and counties and the population density of central urban areas and its surrounding areas significantly increased, while that of non-urban areas such as northeast Chongqing significantly decreased.

scholarly journals Estimating Local Inequality from Nighttime Lights

2021 ◽  
Vol 13 (22) ◽  
pp. 4624
Author(s):  
Nils B. Weidmann ◽  
Gerlinde Theunissen
Keyword(s):  
Local Level ◽  
Population Data ◽  
Demographic And Health Surveys ◽  
Structural Factors ◽  
African Countries ◽  
Nighttime Lights ◽  
Political Perceptions ◽  
Nighttime Light ◽  
Data Collections ◽  
The Relationship

Economic inequality at the local level has been shown to be an important predictor of people’s political perceptions and preferences. However, research on these questions is hampered by the fact that local inequality is difficult to measure and systematic data collections are rare, in particular in countries of the Global South. We propose a new measure of local inequality derived from nighttime light (NTL) emissions data. Our measure corresponds to the local inequality in per capita nighttime light emissions, using VIIRS-derived nighttime light emissions data and spatial population data from WorldPop. We validate our estimates using local inequality estimates from the Demographic and Health Surveys (DHS) for a sample of African countries. Our results show that nightlight-based inequality estimates correspond well to those derived from survey data, and that the relationship is not due to structural factors such as differences between urban and rural regions. We also present predictive results, where we approximate the (survey-based) level of local inequality with our nighttime light indicator. This illustrates how our approach can be used for new cases where no other data are available.

scholarly journals Estimating and Interpreting Fine-Scale Gridded Population Using Random Forest Regression and Multisource Data

2020 ◽  
Vol 9 (6) ◽  
pp. 369
Author(s):  
Yun Zhou ◽  
Mingguo Ma ◽  
Kaifang Shi ◽  
Zhenyu Peng
Keyword(s):  
Random Forest ◽  
Population Distribution ◽  
Population Data ◽  
Estimation Accuracy ◽  
Chinese Government ◽  
Random Forest Regression ◽  
Regression Techniques ◽  
Nighttime Light ◽  
Fine Resolution ◽  
Impervious Areas

Gridded population results at a fine resolution are important for optimizing the allocation of resources and researching population migration. For example, the data are crucial for epidemic control and natural disaster relief. In this study, the random forest model was applied to multisource data to estimate the population distribution in impervious areas at a 30 m spatial resolution in Chongqing, Southwest China. The community population data from the Chinese government were used to validate the estimation accuracy. Compared with the other regression techniques, the random forest regression method produced more accurate results (R2 = 0.7469, RMSE = 2785.04 and p < 0.01). The points of interest (POIs) data played a more important role in the population estimation than the nighttime light images and natural topographical data, particularly in urban settings. Our results support the wide application of our method in mapping densely populated cities in China and other countries with similar characteristics.

scholarly journals Mapping Multi-Temporal Population Distribution in China from 1985 to 2010 Using Landsat Images via Deep Learning

2021 ◽  
Vol 13 (17) ◽  
pp. 3533
Author(s):  
Haoming Zhuang ◽  
Xiaoping Liu ◽  
Yuchao Yan ◽  
Jinpei Ou ◽  
Jialyu He ◽  
...  
Keyword(s):  
Sustainable Development ◽  
Deep Learning ◽  
Large Scale ◽  
Population Distribution ◽  
Population Data ◽  
Google Earth ◽  
Historical Period ◽  
Spatiotemporal Evolution ◽  
Multi Temporal ◽  
Population Mapping

Fine knowledge of the spatiotemporal distribution of the population is fundamental in a wide range of fields, including resource management, disaster response, public health, and urban planning. The United Nations’ Sustainable Development Goals also require the accurate and timely assessment of where people live to formulate, implement, and monitor sustainable development policies. However, due to the lack of appropriate auxiliary datasets and effective methodological frameworks, there are rarely continuous multi-temporal gridded population data over a long historical period to aid in our understanding of the spatiotemporal evolution of the population. In this study, we developed a framework integrating a ResNet-N deep learning architecture, considering neighborhood effects with a vast number of Landsat-5 images from Google Earth Engine for population mapping, to overcome both the data and methodology obstacles associated with rapid multi-temporal population mapping over a long historical period at a large scale. Using this proposed framework in China, we mapped fine-scale multi-temporal gridded population data (1 km × 1 km) of China for the 1985–2010 period with a 5-year interval. The produced multi-temporal population data were validated with available census data and achieved comparable performance. By analyzing the multi-temporal population grids, we revealed the spatiotemporal evolution of population distribution from 1985 to 2010 in China with the characteristic of concentration of the population in big cities and the contraction of small- and medium-sized cities. The framework proposed in this study demonstrates the feasibility of mapping multi-temporal gridded population distribution at a large scale over a long period in a timely and low-cost manner, which is particularly useful in low-income and data-poor areas.

scholarly journals Population Distribution Information System in Tugumulyo District

2020 ◽  
Vol 2 (1) ◽  
pp. 123-130
Author(s):  
Ahmad Syazili ◽  
Ahmad Mutatkin Bakti
Keyword(s):  
Information System ◽  
Population Distribution ◽  
Population Data ◽  
Web Engineering ◽  
Web Based ◽  
The Public ◽  
Social Interests ◽  
Development Methods ◽  
The Village ◽  
Population Mapping

Information on population data is quite important information for stakeholders. Information on population data is often used in various ways such as aiding, or government activities related to population data. The information that is often needed is related to the actual existence of the population in order to conduct a census or other social interests. For this reason, in this study the development of a population mapping information system in Tugumulyo sub-district was made as an effort to provide information to the public regarding population data. In the process of developing information systems, web engineering development methods are used. Web engineering method is a method used to develop a web-based information system. The results of the development in the form of a population distribution information system with the main features of population data information are presented in the form of tables, graphics and maps. In addition, the information displayed can be seen based on the sub-district and also the village or village in the sub-district

scholarly journals Population cluster data to assess the urban-rural split and electrification in Sub-Saharan Africa

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Babak Khavari ◽  
Alexandros Korkovelos ◽  
Andreas Sahlberg ◽  
Mark Howells ◽  
Francesco Fuso Nerini
Keyword(s):  
World Bank ◽  
Population Distribution ◽  
Population Data ◽  
Sub Saharan Africa ◽  
Demographic And Health Surveys ◽  
Middle Income ◽  
Disease Response ◽  
The World Bank ◽  
The World ◽  
Urban Rural

AbstractHuman settlements are usually nucleated around manmade central points or distinctive natural features, forming clusters that vary in shape and size. However, population distribution in geo-sciences is often represented in the form of pixelated rasters. Rasters indicate population density at predefined spatial resolutions, but are unable to capture the actual shape or size of settlements. Here we suggest a methodology that translates high-resolution raster population data into vector-based population clusters. We use open-source data and develop an open-access algorithm tailored for low and middle-income countries with data scarcity issues. Each cluster includes unique characteristics indicating population, electrification rate and urban-rural categorization. Results are validated against national electrification rates provided by the World Bank and data from selected Demographic and Health Surveys (DHS). We find that our modeled national electrification rates are consistent with the rates reported by the World Bank, while the modeled urban/rural classification has 88% accuracy. By delineating settlements, this dataset can complement existing raster population data in studies such as energy planning, urban planning and disease response.

scholarly journals Climatic Stress, Internal Migration, and Syrian Civil War Onset

2019 ◽  
Vol 64 (1) ◽  
pp. 3-31 ◽  
Author(s):  
Konstantin Ash ◽  
Nick Obradovich
Keyword(s):  
Internal Migration ◽  
Meteorological Drought ◽  
Internal Displacement ◽  
Drought Period ◽  
Climatic Stress ◽  
Civil Unrest ◽  
Syrian Civil War ◽  
Nighttime Lights ◽  
Nighttime Light ◽  
Climate Events

Syria recently suffered a once in 500-year meteorological drought followed by one of the worst conflicts of the twenty-first century. We exploit subnational variation in drought impact to examine associations between climatic stress and Syria’s political unrest. Climatic stress may produce instability through both immediate hardship and, indirectly, internal migration. Consistent with the internal migration hypothesis, we find less severely drought-stricken Syrian regions more likely to experience protest. We employ nighttime lights as a proxy for population density to examine the association between climatic stress and internal displacement. We find climatic stress decreased nighttime light intensity during the drought period. Increases in nighttime lights from 2005 to 2010 are associated with added risk of protest in Sunni Arab areas, suggesting an influx of migrants bolstered local grievances. Our findings support the internal migration hypothesis and suggest extreme climate events may impact civil unrest via geographically and temporally indirect paths.

scholarly journals DISTRIBUTION AND SURVIVAL OF MEGAPODIUS REINWARDT FOR ECOTOURISM CONTRIBUTING ON MOYO ISLAND

2018 ◽  
Vol 18 (2) ◽  
Author(s):  
Muhammad Yamin ◽  
Khaeruddin Khaeruddin
Keyword(s):  
Population Distribution ◽  
Economic Value ◽  
Bird Species ◽  
Research Data ◽  
Primary Forest ◽  
Ecological Role ◽  
Active Nest ◽  
In Captivity ◽  
Distribution Mapping

Abstract: Megapodius reinwardt is one of the protected bird species in Indonesia. The protection toward megapodius reinwardt because of limited distribution, high economic value, has an important ecological role, has a unique, difficult breeding in captivity, the population is drastically shrinking and getting scarce.Based on this condition, the purpose of this research is to know population distribution mapping, active nest study, disturber population in order to support the atraction tourism and conservation of Megapodius reinwardt at Moyo island. Survey and observation are used to collect research data. The distributionofMegapodius reinwardt is spread around Moyo Island. The nest is located in the forest and it build by heap of soil with high 150 centimeters until 175 centimeters and diameters up to 825 cm. The location of Megapodius reinwardtnests are mostly in secundary forest than primary forest and savana at 25 m above sea level. The r-product moment correlation coefficient (r = 0,484) conclude that, Varanus sp, Sus barbatus, Prinodon linsang, Haliastur indus, Microhierax fringillarius, and human as predators are not significant to influence the nests and population of Megapodius reinwardtdecrease. So the existence of Megapodius reinwardtmanagement is needed according to in-situ cencervation on Moyo Island forEcotourism Contributing Keywords:   Ecotourism, distribution, Megapodius, survival.

scholarly journals The Random Forest-Based Method of Fine-Resolution Population Spatialization by Using the International Space Station Nighttime Photography and Social Sensing Data

2018 ◽  
Vol 10 (10) ◽  
pp. 1650 ◽  
Author(s):  
Kangning Li ◽  
Yunhao Chen ◽  
Ying Li
Keyword(s):  
Random Forest ◽  
International Space Station ◽  
Population Distribution ◽  
Space Station ◽  
Small Scale ◽  
International Space ◽  
Point Of Interest ◽  
Scale Population ◽  
Fine Resolution ◽  
Population Mapping

Despite the importance of high-resolution population distribution in urban planning, disaster prevention and response, region economic development, and improvement of urban habitant environment, traditional urban investigations mainly focused on large-scale population spatialization by using coarse-resolution nighttime light (NTL) while few efforts were made to fine-resolution population mapping. To address problems of generating small-scale population distribution, this paper proposed a method based on the Random Forest Regression model to spatialize a 25 m population from the International Space Station (ISS) photography and urban function zones generated from social sensing data—point-of-interest (POI). There were three main steps, namely HSL (hue saturation lightness) transformation and saturation calibration of ISS, generating functional-zone maps based on point-of-interest, and spatializing population based on the Random Forest model. After accuracy assessments by comparing with WorldPop, the proposed method was validated as a qualified method to generate fine-resolution population spatial maps. In the discussion, this paper suggested that without help of auxiliary data, NTL cannot be directly employed as a population indicator at small scale. The Variable Importance Measure of the RF model confirmed the correlation between features and population and further demonstrated that urban functions performed better than LULC (Land Use and Land Cover) in small-scale population mapping. Urban height was also shown to improve the performance of population disaggregation due to its compensation of building volume. To sum up, this proposed method showed great potential to disaggregate fine-resolution population and other urban socio-economic attributes.

scholarly journals Assessing the Ability of Luojia 1-01 Imagery to Detect Feeble Nighttime Lights

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3708 ◽  
Author(s):  
Li ◽  
Liu ◽  
Chen ◽  
Sun
Keyword(s):  
Infrared Imaging ◽  
Detection Accuracy ◽  
Application Research ◽  
Threshold Method ◽  
Minimum Area ◽  
Nighttime Lights ◽  
Nighttime Light ◽  
Data Source ◽  
Meteorological Satellite ◽  
Cutoff Threshold

The Luojia1-01 (LJ1-01) satellite launched on June 2, 2018 provides a new option for nighttime light (NTL) application research. In this paper, four types of human settlements, such as cities, counties, towns and villages, are sampled to evaluate the potential of LJ1-01 to detect feeble NTL by comparing with the NTL images from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) and the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-Orbiting Partnership Satellite. First, the landscape indices and cutoff threshold method are applied to enhance signal-noise ratio (SNR). Then, the detection accuracy of samples is evaluated to determine the optimal cutoff threshold for each NTL data source. After that, the spatial correspondence of different NTL images and the area consistency between the samples and NTL footprints are compared. Finally, after the discussion of feeble NTL detection and the influence of clouds, moonlight and image composites, it can be concluded that LJ1-01 is more suitable for detection feeble NTL objects, while great importance should be attached to the measures to eliminate the noise in LJ1-01 image and make LJ1-01 more widely used: (1) In the study area, a suitable cutoff threshold of LJ1-01 image can be set to 0.1 nano-Wcm−2sr−1, which is lower than that of VIIRS image (0.3 nano-Wcm−2sr−1), and this enables LJ1-01 to reserve more information of NTL, especially the feeble NTL. Moreover, the minimum area that can be identified by NTL footprints from LJ1-01 is 0.02 km2, while that of VIIRS and DMSP are 0.3 km2 and 4.5 km2, respectively. (2) The cutoff threshold method can identify the range of NTL with more noise, but cannot eliminate the noise separately. The filtering method and the image composition method may play more important role in the applications of LJ1-01 data.

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